OpenRaiser/Intern-Atlas huggingface dataset:
Rethinking the Detail-Preserved Completion of Complex Tubular Structures based on Point Cloud: a Dataset and a Benchmark
Venue: arXiv cs.CV 2025
Year: 2025

Complex tubular structures are essential in medical imaging and computer-assisted diagnosis, where their integrity enhances anatomical visualization and lesion detection. However, existing segmentation algorithms struggle with structural discontinuities, particularly in severe clinical cases such as coronary artery stenosis and vessel occlusions, which leads to undesired discontinuity and compromising downstream diagnostic accuracy. Therefore, it is imperative to reconnect discontinuous structures to ensure their completeness. In this study, we explore the tubular structure completion based on point cloud for the first time and establish a Point Cloud-based Coronary Artery Completion (PC-CAC) dataset, which is derived from real clinical data. This dataset provides a novel benchmark for tubular structure completion. Additionally, we propose TSRNet, a Tubular Structure Reconnection Network that integrates a detail-preservated feature extractor, a multiple dense refinement strategy, and a global-to-local loss function to ensure accurate reconnection while maintaining structural integrity. Comprehensive experiments on our PC-CAC and two additional public datasets (PC-ImageCAS and PC-PTR) demonstrate that our method consistently outperforms state-of-the-art approaches across multiple evaluation metrics, setting a new benchmark for point cloud-based tubular structure reconstruction. Our benchmark is available at https://github.com/YaoleiQi/PCCAC.
Heterogeneous co-occurrence embedding for visual information exploration
Venue: arXiv cs.LG 2025
Year: 2025

This paper proposes an embedding method for co-occurrence data aimed at visual information exploration. We consider cases where co-occurrence probabilities are measured between pairs of elements from heterogeneous domains. The proposed method maps these heterogeneous elements into corresponding two-dimensional latent spaces, enabling visualization of asymmetric relationships between the domains. The key idea is to embed the elements in a way that maximizes their mutual information, thereby preserving the original dependency structure as much as possible. This approach can be naturally extended to cases involving three or more domains, using a generalization of mutual information known as total correlation. For inter-domain analysis, we also propose a visualization method that assigns colors to the latent spaces based on conditional probabilities, allowing users to explore asymmetric relationships interactively. We demonstrate the utility of the method through applications to an adjective-noun dataset, the NeurIPS dataset, and a subject-verb-object dataset, showcasing both intra- and inter-domain analysis.
A Taxonomy of Transcendence
Venue: arXiv cs.AI 2025
Year: 2025

Although language models are trained to mimic humans, the resulting systems display capabilities beyond the scope of any one person. To understand this phenomenon, we use a controlled setting to identify properties of the training data that lead a model to transcend the performance of its data sources. We build on previous work to outline three modes of transcendence, which we call skill denoising, skill selection, and skill generalization. We then introduce a knowledge graph-based setting in which simulated experts generate data based on their individual expertise. We highlight several aspects of data diversity that help to enable the model's transcendent capabilities. Additionally, our data generation setting offers a controlled testbed that we hope is valuable for future research in the area.
LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios
Venue: arXiv cs.AI 2025
Year: 2025

Recent advances in the intrinsic reasoning capabilities of large language models (LLMs) have given rise to LLM-based agent systems that exhibit near-human performance on a variety of automated tasks. However, although these systems share similarities in terms of their use of LLMs, different reasoning frameworks of the agent system steer and organize the reasoning process in different ways. In this survey, we propose a systematic taxonomy that decomposes agentic reasoning frameworks and analyze how these frameworks dominate framework-level reasoning by comparing their applications across different scenarios. Specifically, we propose an unified formal language to further classify agentic reasoning systems into single-agent methods, tool-based methods, and multi-agent methods. After that, we provide a comprehensive review of their key application scenarios in scientific discovery, healthcare, software engineering, social simulation, and economics. We also analyze the characteristic features of each framework and summarize different evaluation strategies. Our survey aims to provide the research community with a panoramic view to facilitate understanding of the strengths, suitable scenarios, and evaluation practices of different agentic reasoning frameworks.
Database Normalization via Dual-LLM Self-Refinement
Venue: arXiv cs.DB 2025
Year: 2025

Database normalization is crucial to preserving data integrity. However, it is time-consuming and error-prone, as it is typically performed manually by data engineers. To this end, we present Miffie, a database normalization framework that leverages the capability of large language models. Miffie enables automated data normalization without human effort while preserving high accuracy. The core of Miffie is a dual-model self-refinement architecture that combines the best-performing models for normalized schema generation and verification, respectively. The generation module eliminates anomalies based on the feedback of the verification module until the output schema satisfies the requirement for normalization. We also carefully design task-specific zero-shot prompts to guide the models for achieving both high accuracy and cost efficiency. Experimental results show that Miffie can normalize complex database schemas while maintaining high accuracy.
Adaptive Ensemble Learning with Gaussian Copula for Load Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning (ML) is capable of accurate Load Forecasting from complete data. However, there are many uncertainties that affect data collection, leading to sparsity. This article proposed a model called Adaptive Ensemble Learning with Gaussian Copula to deal with sparsity, which contains three modules: data complementation, ML construction, and adaptive ensemble. First, it applies Gaussian Copula to eliminate sparsity. Then, we utilise five ML models to make predictions individually. Finally, it employs adaptive ensemble to get final weighted-sum result. Experiments have demonstrated that our model are robust.
Enhancing LLM-Based Social Bot via an Adversarial Learning Framework
Venue: arXiv cs.SI 2025
Year: 2025

Developing Large Language Model (LLM) agents that exhibit human-like behavior, encompassing not only individual heterogeneity rooted in unique user profiles but also adaptive response to socially connected neighbors, is a significant research challenge. Social media platforms, with their diverse user data and explicit social structures, provide an ideal testbed for such investigations. This paper introduces EvoBot, an \textbf{Evo}lving LLM-based social \textbf{Bot} that significantly enhances human-like generative capabilities through a novel adversarial learning framework. EvoBot is initialized by Supervised Fine-Tuning (SFT) on representative data from social media and then iteratively refines its generation of sophisticated, human-like content via Direct Preference Optimization (DPO). This refinement is guided by feedback from a co-adapting \textbf{Detector} which concurrently improves its ability to distinguish EvoBot from humans, thereby creating an increasingly challenging learning environment for EvoBot. Experiments demonstrate that EvoBot generates content aligned with diverse user profiles, increasingly bypassing the co-adapting Detector through human-like expression. Moreover, it exhibits strong social responsiveness, more accurately modeling real-world opinion dynamics and information spread in multi-agent simulations. The framework also yields a more robust Detector, underscoring its broader utility for both advanced agent development and related detection tasks. The code is available at https://github.com/kfq20/EvoBot.
How Do LLM-Generated Texts Impact Term-Based Retrieval Models?
Venue: arXiv cs.IR 2025
Year: 2025

As more content generated by large language models (LLMs) floods into the Internet, information retrieval (IR) systems now face the challenge of distinguishing and handling a blend of human-authored and machine-generated texts. Recent studies suggest that neural retrievers may exhibit a preferential inclination toward LLM-generated content, while classic term-based retrievers like BM25 tend to favor human-written documents. This paper investigates the influence of LLM-generated content on term-based retrieval models, which are valued for their efficiency and robust generalization across domains. Our linguistic analysis reveals that LLM-generated texts exhibit smoother high-frequency and steeper low-frequency Zipf slopes, higher term specificity, and greater document-level diversity. These traits are aligned with LLMs being trained to optimize reader experience through diverse and precise expressions. Our study further explores whether term-based retrieval models demonstrate source bias, concluding that these models prioritize documents whose term distributions closely correspond to those of the queries, rather than displaying an inherent source bias. This work provides a foundation for understanding and addressing potential biases in term-based IR systems managing mixed-source content.
Instant Preference Alignment for Text-to-Image Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-image (T2I) generation has greatly enhanced creative expression, yet achieving preference-aligned generation in a real-time and training-free manner remains challenging. Previous methods often rely on static, pre-collected preferences or fine-tuning, limiting adaptability to evolving and nuanced user intents. In this paper, we highlight the need for instant preference-aligned T2I generation and propose a training-free framework grounded in multimodal large language model (MLLM) priors. Our framework decouples the task into two components: preference understanding and preference-guided generation. For preference understanding, we leverage MLLMs to automatically extract global preference signals from a reference image and enrich a given prompt using structured instruction design. Our approach supports broader and more fine-grained coverage of user preferences than existing methods. For preference-guided generation, we integrate global keyword-based control and local region-aware cross-attention modulation to steer the diffusion model without additional training, enabling precise alignment across both global attributes and local elements. The entire framework supports multi-round interactive refinement, facilitating real-time and context-aware image generation. Extensive experiments on the Viper dataset and our collected benchmark demonstrate that our method outperforms prior approaches in both quantitative metrics and human evaluations, and opens up new possibilities for dialog-based generation and MLLM-diffusion integration.
CMFDNet: Cross-Mamba and Feature Discovery Network for Polyp Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Automated colonic polyp segmentation is crucial for assisting doctors in screening of precancerous polyps and diagnosis of colorectal neoplasms. Although existing methods have achieved promising results, polyp segmentation remains hindered by the following limitations,including: (1) significant variation in polyp shapes and sizes, (2) indistinct boundaries between polyps and adjacent tissues, and (3) small-sized polyps are easily overlooked during the segmentation process. Driven by these practical difficulties, an innovative architecture, CMFDNet, is proposed with the CMD module, MSA module, and FD module. The CMD module, serving as an innovative decoder, introduces a cross-scanning method to reduce blurry boundaries. The MSA module adopts a multi-branch parallel structure to enhance the recognition ability for polyps with diverse geometries and scale distributions. The FD module establishes dependencies among all decoder features to alleviate the under-detection of polyps with small-scale features. Experimental results show that CMFDNet outperforms six SOTA methods used for comparison, especially on ETIS and ColonDB datasets, where mDice scores exceed the best SOTA method by 1.83% and 1.55%, respectively.
SMITE: Enhancing Fairness in LLMs through Optimal In-Context Example Selection via Dynamic Validation
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are widely used for downstream tasks such as tabular classification, where ensuring fairness in their outputs is critical for inclusivity, equal representation, and responsible AI deployment. This study introduces a novel approach to enhancing LLM performance and fairness through the concept of a dynamic validation set, which evolves alongside the test set, replacing the traditional static validation approach. We also propose an iterative algorithm, SMITE, to select optimal in-context examples, with each example set validated against its corresponding dynamic validation set. The in-context set with the lowest total error is used as the final demonstration set. Our experiments across four different LLMs show that our proposed techniques significantly improve both predictive accuracy and fairness compared to baseline methods. To our knowledge, this is the first study to apply dynamic validation in the context of in-context learning for LLMs.
Two-Timescale Learning for Pilot-Free ISAC Systems
Venue: arXiv cs.IT 2025
Year: 2025

A pilot-free integrated sensing and communication (ISAC) system is investigated, in which phase-modulated continuous wave (PMCW) and non-orthogonal multiple access (NOMA) waveforms are co-designed to achieve simultaneous target sensing and data transmission. To enhance effective data throughput (i.e., Goodput) in PMCW-NOMA ISAC systems, we propose a deep learning-based receiver architecture, termed two-timescale Transformer (T3former), which leverages a Transformer architecture to perform joint channel estimation and multi-user signal detection without the need for dedicated pilot signals. By treating the deterministic structure of the PMCW waveform as an implicit pilot, the proposed T3former eliminates the overhead associated with traditional pilot-based methods. The proposed T3former processes the received PMCW-NOMA signals on two distinct timescales, where a fine-grained attention mechanism captures local features across the fast-time dimension, while a coarse-grained mechanism aggregates global spatio-temporal dependencies of the slow-time dimension. Numerical results demonstrate that the proposed T3former significantly outperforms traditional successive interference cancellation (SIC) receivers, which avoids inherent error propagation in SIC. Specifically, the proposed T3former achieves a substantially lower bit error rate and a higher Goodput, approaching the theoretical maximum capacity of a pilot-free system.
Puzzle: Scheduling Multiple Deep Learning Models on Mobile Device with Heterogeneous Processors
Venue: arXiv cs.LG 2025
Year: 2025

As deep learning models are increasingly deployed on mobile devices, modern mobile devices incorporate deep learning-specific accelerators to handle the growing computational demands, thus increasing their hardware heterogeneity. However, existing works on scheduling deep learning workloads across these processors have significant limitations: most studies focus on single-model scenarios rather than realistic multi-model scenarios, overlook performance variations from different hardware/software configurations, and struggle with accurate execution time estimation. To address these challenges, we propose a novel genetic algorithm-based methodology for scheduling multiple deep learning networks on heterogeneous processors by partitioning the networks into multiple subgraphs. Our approach incorporates three different types of chromosomes for partition/mapping/priority exploration, and leverages device-in-the-loop profiling and evaluation for accurate execution time estimation. Based on this methodology, our system, Puzzle, demonstrates superior performance in extensive evaluations with randomly generated scenarios involving nine state-of-the-art networks. The results demonstrate Puzzle can support 3.7 and 2.2 times higher request frequency on average compared to the two heuristic baselines, NPU Only and Best Mapping, respectively, while satisfying the equivalent level of real-time requirements.
UniSino: Physics-Driven Foundational Model for Universal CT Sinogram Standardization
Venue: arXiv cs.CV 2025
Year: 2025

During raw-data acquisition in CT imaging, diverse factors can degrade the collected sinograms, with undersampling and noise leading to severe artifacts and noise in reconstructed images and compromising diagnostic accuracy. Conventional correction methods rely on manually designed algorithms or fixed empirical parameters, but these approaches often lack generalizability across heterogeneous artifact types. To address these limitations, we propose UniSino, a foundation model for universal CT sinogram standardization. Unlike existing foundational models that operate in image domain, UniSino directly standardizes data in the projection domain, which enables stronger generalization across diverse undersampling scenarios. Its training framework incorporates the physical characteristics of sinograms, enhancing generalization and enabling robust performance across multiple subtasks spanning four benchmark datasets. Experimental results demonstrate thatUniSino achieves superior reconstruction quality both single and mixed undersampling case, demonstrating exceptional robustness and generalization in sinogram enhancement for CT imaging. The code is available at: https://github.com/yqx7150/UniSino.
Limitations of Normalization in Attention Mechanism
Venue: arXiv cs.LG 2025
Year: 2025

This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the identification of the model's selective ability and the geometric separation involved in token selection. Our analysis includes explicit bounds on distances and separation criteria for token vectors under softmax scaling. Through experiments with pre-trained GPT-2 model, we empirically validate our theoretical results and analyze key behaviors of the attention mechanism. Notably, we demonstrate that as the number of selected tokens increases, the model's ability to distinguish informative tokens declines, often converging toward a uniform selection pattern. We also show that gradient sensitivity under softmax normalization presents challenges during training, especially at low temperature settings. These findings advance current understanding of softmax-based attention mechanism and motivate the need for more robust normalization and selection strategies in future attention architectures.
Software Unclonable Functions for IoT Devices Identification and Security
Venue: arXiv cs.CR 2025
Year: 2025

In the evolving landscape of IoT ecosystem, distinguishing between legitimate and compromised devices is a critical challenge. This research investigates the effectiveness of hardware performance counter (HPC)-derived signatures' uniqueness under the umbrella of a concept that we introduced as software unclonable functions (SUFs).
Certificates and Witnesses for Multi-objective ω-regular Queries in Markov Decision Processes
Venue: arXiv cs.LO 2025
Year: 2025

Multi-objective probabilistic model checking is a powerful technique for verifying stochastic systems against multiple (potentially conflicting) properties. To enhance the trustworthiness and explainability of model checking tools, we present independently checkable certificates and witnesses for multi-objective ω-regular queries in Markov decision processes. For the certification, we extend and improve existing certificates for the decomposition of maximal end components and reachability properties. We then derive mixed-integer linear programs (MILPs) for finding minimal witnessing subsystems. For the special case of Markov chains and LTL properties, we use unambiguous Büchi automata to find witnesses, resulting in an algorithm that requires single-exponential space. Existing approaches based on deterministic automata require doubly-exponential space in the worst case. Finally, we consider the practical computation of our certificates and witnesses and provide an implementation of the developed techniques, along with an experimental evaluation, demonstrating the efficacy of our techniques.
Retrieval Feedback Memory Enhancement Large Model Retrieval Generation Method
Venue: arXiv cs.IR 2025
Year: 2025

Large Language Models (LLMs) have shown remarkable capabilities across diverse tasks, yet they face inherent limitations such as constrained parametric knowledge and high retraining costs. Retrieval-Augmented Generation (RAG) augments the generation process by retrieving externally stored knowledge absent from the models internal parameters. However, RAG methods face challenges such as information loss and redundant retrievals during multi-round queries, accompanying the difficulties in precisely characterizing knowledge gaps for complex tasks. To address these problems, we propose Retrieval Feedback and Memory Retrieval Augmented Generation(RFM-RAG), which transforms the stateless retrieval of previous methods into stateful continuous knowledge management by constructing a dynamic evidence pool. Specifically, our method generates refined queries describing the models knowledge gaps using relational triples from questions and evidence from the dynamic evidence pool; Retrieves critical external knowledge to iteratively update this evidence pool; Employs a R-Feedback Model to evaluate evidence completeness until convergence. Compared to traditional RAG methods, our approach enables persistent storage of retrieved passages and effectively distills key information from passages to construct clearly new queries. Experiments on three public QA benchmarks demonstrate that RFM-RAG outperforms previous methods and improves overall system accuracy.
Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs
Venue: arXiv cs.CL 2025
Year: 2025

With the rise of Speech Large Language Models (SpeechLLMs), two dominant approaches have emerged for speech processing: discrete tokens and continuous features. Each approach has demonstrated strong capabilities in audio-related processing tasks. However, the performance gap between these two paradigms has not been thoroughly explored. To address this gap, we present a fair comparison of self-supervised learning (SSL)-based discrete and continuous features under the same experimental settings. We evaluate their performance across six spoken language understanding-related tasks using both small and large-scale LLMs (Qwen1.5-0.5B and Llama3.1-8B). We further conduct in-depth analyses, including efficient comparison, SSL layer analysis, LLM layer analysis, and robustness comparison. Our findings reveal that continuous features generally outperform discrete tokens in various tasks. Each speech processing method exhibits distinct characteristics and patterns in how it learns and processes speech information. We hope our results will provide valuable insights to advance spoken language understanding in SpeechLLMs.